2 research outputs found
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
While generative adversarial networks (GANs) can successfully produce
high-quality images, they can be challenging to control. Simplifying GAN-based
image generation is critical for their adoption in graphic design and artistic
work. This goal has led to significant interest in methods that can intuitively
control the appearance of images generated by GANs. In this paper, we present
HistoGAN, a color histogram-based method for controlling GAN-generated images'
colors. We focus on color histograms as they provide an intuitive way to
describe image color while remaining decoupled from domain-specific semantics.
Specifically, we introduce an effective modification of the recent StyleGAN
architecture to control the colors of GAN-generated images specified by a
target color histogram feature. We then describe how to expand HistoGAN to
recolor real images. For image recoloring, we jointly train an encoder network
along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised
approach trained to encourage the network to keep the original image's content
while changing the colors based on the given target histogram. We show that
this histogram-based approach offers a better way to control GAN-generated and
real images' colors while producing more compelling results compared to
existing alternative strategies.Comment: CVPR 202
Palette-based image decomposition, harmonization, and color transfer
We present a palette-based framework for color composition for visual
applications. Color composition is a critical aspect of visual applications in
art, design, and visualization. The color wheel is often used to explain
pleasing color combinations in geometric terms, and, in digital design, to
provide a user interface to visualize and manipulate colors. We abstract
relationships between palette colors as a compact set of axes describing
harmonic templates over perceptually uniform color wheels. Our framework
provides a basis for a variety of color-aware image operations, such as color
harmonization and color transfer, and can be applied to videos. To enable our
approach, we introduce an extremely scalable and efficient yet simple
palette-based image decomposition algorithm. Our approach is based on the
geometry of images in RGBXY-space. This new geometric approach is orders of
magnitude more efficient than previous work and requires no numerical
optimization. We demonstrate a real-time layer decomposition tool. After
preprocessing, our algorithm can decompose 6 MP images into layers in 20
milliseconds. We also conducted three large-scale, wide-ranging perceptual
studies on the perception of harmonic colors and harmonization algorithms.Comment: 17 pages, 25 figure